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Predictability-Computability-Stability workflow for veridical data science in the age of artificial intelligence.

Zachary T Rewolinski¹, Bin Yu^1,2

¹Department of Statistics, UC Berkeley, Berkeley, CA, USA.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences

|May 28, 2026

Summary

This summary is machine-generated.

The Predictability-Computability-Stability (PCS) framework enhances data science reproducibility by addressing uncertainty in the data science life cycle (DSLC). This updated workflow guides practitioners, improving the reliability of artificial intelligence (AI) findings.

Keywords:

computation documentation generative AI prediction reality check reproducibility robustness stability uncertainty workflow

Related Experiment Videos

Area of Science:

Data Science
Artificial Intelligence (AI)
Statistical Workflow

Background:

Artificial intelligence (AI) is transforming various scientific and medical domains.
Replicability of AI-driven findings is often hindered by uncertainty within the data science life cycle (DSLC).
Traditional statistical methods inadequately address the inherent uncertainty in the DSLC.

Purpose of the Study:

To present an updated and streamlined Predictability-Computability-Stability (PCS) workflow for veridical (truthful) data science (VDS).
To enhance the PCS workflow with guided use of generative AI (GenAI) for practitioners.
To demonstrate the PCS framework's application and its impact on prediction uncertainty.

Main Methods:

An updated and streamlined Predictability-Computability-Stability (PCS) workflow is detailed.
Generative AI (GenAI) is integrated to guide practitioners through the PCS workflow.
A running example and a case study illustrate the PCS framework's application and uncertainty analysis.

Main Results:

The PCS framework offers a principled approach to managing uncertainty throughout the DSLC.
The updated workflow, enhanced with GenAI, provides practical guidance for data scientists.
A case study highlights how data cleaning choices introduce uncertainty into downstream predictions.

Conclusions:

The PCS framework is crucial for achieving veridical (truthful) data science (VDS) and improving AI reproducibility.
The enhanced PCS workflow empowers practitioners to better navigate DSLC uncertainties.
Understanding and quantifying uncertainty is essential for reliable AI-driven decision-making.